In the News

August 03, 2012

E-Commerce Style Big Data Analytics Meet Brick And Mortar Retailers

August 3, 2012

Forbes online; Tom Groenfeldt, Contributor -Online stores have been testing their sites for years, playing with the design, figuring out where items should be positioned to catch the eyes of shoppers, figuring out how to position first, second and third offers or suggest followup purchases.

Brick and mortar stores have not only been stuck without those types of analytics, they have also suffered the ignominy of serving as real-world catalog showrooms for the e-commerce companies — buyers will visit stores like Best Buy and often, in the store of all the nerve, check inventory against pricing from Amazon.

Now the physical stores have tools to collect data on their shoppers by monitoring their movement, and their pauses, as they move around the aisles of real stores.RetailNext is one of the providers of shopper intelligence through video that can provide up to 10,000 data points per store visitor, allowing stores to, for example, develop heat maps so they can put the items they want to sell in areas of the stores with the most traffic. Coming soon — cameras that can detect a shopper’s mood through facial expressions. Try that online!

Physical stores still account for more than 95 percent of retail; Best Buy sold $45.9 billion through its stores in 2011 compared for $48 billion for Amazon. AdWeek predicts that physical stores will still account for at least 80 percent of retail sales a decades from now.

Although RetailNext’s hardware demands are minimal — it can often use at least some existing security cameras, the data it generates is not. The cameras feed video into a server inside the store where it is abstracted a layer to turn people into points in physical space. That reduced lever of data can be sent to an aggregation server in the store or out at a data center. The raw data that RetailNext collects works out to 57 petabytes per year from 300 million shoppers a year at the 50 retail chains it works with.

“In-store analytics now rivals online analytics in its depth, reliability, and usefulness,” wrote CEO Alexei Agratchev in VentureBeat. Stores can see where shoppers go, where they linger, detect whether they are shopping alone or with friends or children, and match shopping to weather. By equipping staff with RFID chips, they can see if sales people are interacting with customers.

Tim Callan, chief marketing officer at RetailNext, said web sites have been using analytics since the mid-1990s.

“But people who run brick and mortar stores have not had the technology to optimize their stores. They have relied on crude tools such as walking around the store to see what they think is working well, but they have not been able to optimize the way e-tailers could.”

RetailNext, which has been around for five years, is now in more than 50 retail chains, he said, and users have seen double-digit increases in sales with it, plus substantial reductions in theft, or inventory shrinkage in retailer-speak. Brookstone figures it has saved about $1 million a year in shrinkage since installing RetailNext and American Apparel said the system pays for itself in four months through reduced losses.

Using multiple video cameras, RetailNext can track individual shoppers as they move through the store and then tie their path to the point of sale (POS) records from cash registers. So the system can measure how long a shopper stands in front of laundry soap — called a dwell — and link it to the POS.

Duty free stores in airports use it to see how people are shopping — fliers heading to Europe buy differently from those going to South America.

One company with small stores set in malls found the space just inside the entry was a dead zone, so they moved the popular items further inside the store. Another store had duplicate inventory on the walls and under glass on large tables, and couldn’t tell which display sold more effectively. They were about to get rid of the wall displays when they decided to check traffic with RetailNext.

“They did a heat map and found the tables were awkward and hard for customers to shop and the walls were selling product, so they made the floor displays smaller and easier for customers to walk through to reach the wall displays.”

High volume retailers like drugstore chains and grocery stores, have found that end caps — the displays set up at the end of aisles — sell, but stores want analytics to see if they cannibalize sales of the same item stacked halfway down the aisle or if the contribute to additional sales.

The manager of a men’s clothing section needs to know whether it is shopped by men or women, and if that changes during the day, because it will affect the stocking and display.

, a St. Paul, MN-based outdoor chain that has physical stores, catalogs and a large internet business, said a basic function they use RetailNext for is customer counting so they can make sure stores are staffed appropriately for the traffic at all times of the day.

A Gander Mountain store in an areas with little other retail may experience twice the traffic of a store in an areas with a lot of other shops, said Robin Smith, director of technology services. They think that more staff at the busier stores could generate more sales. RetailNext has provided better data than a previous system Gander Mountain used.

That’s because a big part of Gander Mountain’s appeal is that it has staff with great experience in their fields. In the fishing department, a store staffer is likely to know the areas’s lakes and streams and be able to recommend not only equipment and lures, but best times of the day, or the year, for different fish. All that expertise is wasted, however, if the store is understaffed and a customer can’t get to talk to the sales person.

“Our goal is a better customer experience,” said Schindler.

Using RetailNext imagery they are changing some of their end of aisle displays. The videos showed people streaming to the left and right past one beautiful display because it was too close inside the entrance, and shoppers were moving so they wouldn’t block the people behind them. (This is the Midwest, where people really do show such consideration.) With another display, said Schindler, the video shows it was too close to some store fixtures, so they moved it and shoppers gave it more attention.

Some of the data from RetailNext confirms what store managers thought was happening, but in other cases it has caused them to rethink their stores.

“It helps us make a better correlations between transaction data and traffic,” said Schindler. “So now we can find opportunities we might be missing.”

RetailNext demands minimal hardware; it can often use at least some existing security cameras. It feeds the video into a server inside the store where it is abstracted a layer to turn people into points in physical space. That reduced level of data can be sent to an aggregation server in the store or out at a data center. The raw data that RetailNext collects works out to 57 petabytes per year from thousands of stores.

Gander Mountain has only been using RetailNext for seven months, so they are still learning, said Smith. Gander Mountain is redesigning stores to make them appealing beyond single-minded hunters and fishermen and attractive to casual outdoor enthusiasts, including women, who are interested in attractive, durable clothing and gear.

“We want to serve a customer who comes into the store for a camping or fishing trip, said Schindler, “and provide them with the advice they need from experienced sales staff, so they get the equipment they need to make their trip better.”